A machine learning based method for the prediction of secretory proteins using amino acid composition, their order and similarity-search.
Most of the prediction methods for secretory proteins require the presence of a correct N-terminal end of the preprotein for correct classification. As large scale genome sequencing projects sometimes assign the 5'-end of genes incorrectly, many proteins are encoded without the correct N-terminus leading to incorrect prediction. In this study, a systematic attempt has been made to predict secretory proteins irrespective of presence or absence of N-terminal signal peptides (also known as classical and non-classical secreted proteins respectively), using machine-learning techniques; artificial neural network (ANN) and support vector machine (SVM). We trained and tested our methods on a dataset of 3321 secretory and 3654 non-secretory mammalian proteins using five-fold cross-validation technique. First, ANN-based modules have been developed for predicting secretory proteins using 33 physico-chemical properties, amino acid composition and dipeptide composition and achieved accuracies of 73.1%, 76.1% and 77.1%, respectively. Similarly, SVM-based modules using 33 physico-chemical properties, amino acid, and dipeptide composition have been able to achieve accuracies of 77.4%, 79.4% and 79.9%, respectively. In addition, BLAST and PSI-BLAST modules designed for predicting secretory proteins based on similarity search achieved 23.4% and 26.9% accuracy, respectively. Finally, we developed a hybrid-approach by integrating amino acid and dipeptide composition based SVM modules and PSI-BLAST module that increased the accuracy to 83.2%, which is significantly better than individual modules. We also achieved high sensitivity of 60.4% with low value of 5% false positive predictions using hybrid module. A web server SRTpred has been developed based on above study for predicting classical and non-classical secreted proteins from whole sequence of mammalian proteins, which is available from http://www.imtech.res.in/raghava/srtpred/.
Garg
Aarti
A
Institute of Microbial Technology, Sector 39A, Chandigarh, India.
Raghava
Gajendra P S
GP
D000465:Algorithms; D000596:Amino Acids / Q000737:chemistry*; D000818:Animals; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D030562:Databases, Protein; D020407:Internet; D011506:Proteins / Q000737:chemistry* / Q000235:genetics; D012372:ROC Curve; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D020539:Sequence Analysis, Protein / Q000379:methods*; D012984:Software
D000596:Amino Acids; D011506:Proteins